Article | June 18, 2021
Data is an important asset. Data leads to innovation and organizations tend to compete for leading these innovations on a global scale. Today, every business requires data and insights to stay relevant in the market. Big Data has a huge impact on the way organizations conduct their businesses. Big Data is used in different enterprises like travel, healthcare, manufacturing, governments, and more. If they need to determine their audience, understand what clients want, forecast the needs of the customers and the clients, AI and big data analysis is vital to every decision-making scenario. When companies process the collected data accurately, they get the desired results, which leads them to their desired goals.
The term Big Data has been around since the 1990s. By the time we could fully comprehend it, Big Data had already amassed a huge amount of stored data. If this data is analyzed properly, it would reveal valuable industry insights into the industry to which the data belonged.
IT professionals and computer scientists realized that going through all of the data and analyzing it for the purpose was too big of a task for humans to undertake. When artificial intelligence (AI) algorithm came into the picture, it accomplished analyzing the accumulated data and deriving insights. The use of AI in Big Data is fundamental to get desired results for organizations.
According to Northeastern University, the amount of data in the world was 4.4 zettabytes in 2013. By of 2020, the data rose to 44 zettabytes.
When there is this amount of data produced globally, this information is invaluable to the enterprises and now can leverage AI algorithms to process it. Because of this, the companies can understand and influence customer behavior. By 2018, over 50% of countries had adopted Big Data.
Let us understand what Big Data, convergence of big data and AI, and impact of AI on big data analytics.
Understanding Big Data
In simple words, Big Data is a term that comprises every tool and process that helps people use and manage vast sets of data. According to Gartner, Big Data is a “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision-making, and process automation.”
The concept of Big Data was created to capture trends, preferences, and user behavior in one place called the data lake. Big Data in enterprises can help them analyze and configure their customers’ motivations and come up with new ideas for the creation of new offerings. Big Data studies different methods of extracting, analyzing, or dealing with data sets that are too complicated for traditional data processing systems. To analyze a large amount of data requires a system designed to stretch its extraction and analysis capability.
Data is everywhere. This stockpile of data can give us insights and business analytics to the industry belonging to the data set. Therefore, the AI algorithms are written to benefit from large and complex data.
Importance of Big Data
Data is an integral part of understanding customer demographics and their motivations.
When customers interact with technology in active or passive manner, these actions create a new set of data. What contributes to this data creation is what they carry with them every day - their smartphones. Their cameras, credit cards, purchased products all contribute to their growing data profile. A correctly done analysis can tell a lot about their behavior patterns, personality, and events in the customer’s life. Companies can use this information to rethink their strategies, improve on their product, and create targeted marketing campaigns, which would ultimately lead them to their target customer.
Industry experts, for years and years, have discussed Big Data and its impact on businesses. Only in recent years, however, has it become possible to calculate that impact. Algorithms and software can now analyze large datasets quickly and efficiently.The forty-four zettabyte of data will only quadruple in the coming years. This collection and analysis of the data will help companies get the AI insights that will aid them in generating profits and be future-ready.
Organizations have been using Big Data for a long time. Here’s how those organizations are using Big Data to drive success:
Answering customer questions
Using big data and analytics, companies can learn the following things:
• What do customers want?
• Where are they missing out on?
• Who are their best and loyal customers?
• Why people choose different products?
Every day, as organizations gather more information, they can get more insights into sales and marketing. Once they get this data, they can optimize their campaigns to suit the customer’s needs. Learning from their online habits and with correct analysis, companies can send personalized promotional emails. These emails may prompt this target audience to convert into full-time customers.
Making confident decisions
As companies grow, they all need to make complex decisions. With in-depth analysis of marketplace knowledge, industry, and customers, Big Data can help you make confident choices. Big Data gives you a complete overview of everything you need to know. With the help of this, you can launch your marketing campaign or launch a new product in the market, or make a focused decision to generate the highest ROI. Once you add machine learning and AI to the mix, your Big Data collections can form a neural network to help your AI suggest useful company changes.
Optimizing and Understanding Business Processes
Cloud computing and machine learning help you to stay ahead by identifying opportunities in your company’s practices. Big Data analytics can tell you if your email strategy is working even when your social media marketing isn’t gaining you any following. You can also check which parts of your company culture have the right impact and result in the desired turnover. The existing evidence can help you make quick decisions and ensure you spend more of your budget on things that help your business grow.
Convergence of Big Data and AI
Big Data and Artificial Intelligence have a synergistic relationship. Data powers AI. The constantly evolving data sets or Big Data makes it possible for machine learning applications to learn and acquire new skills. This is what they were built to do. Big Data’s role in AI is supplying algorithms with all the essential information for developing and improving features, pattern recognition capabilities.
AI and machine learning use data that has been cleansed of duplicate and unnecessary data. This clean and high-quality big data is then utilized to create and train intelligent AI algorithms, neural networks, and predictive models.
AI applications rarely stop working and learning. Once the “initial training” is done (initial training is preparing already collected data), they adjust their work as and when the data changes. This makes it necessary for data to be constantly collected.
When it comes to businesses using this technology, AI helps them use Big Data for analytics by making advanced tools accessible and obtainable to help users gain insights that would otherwise have been hidden in the huge amount of data. Once firms and businesses gain a hold on using AI and Big Data, they can provide decision-makers with a clear understanding of factors that affect their businesses.
Impact of AI on Big Data Analytics
AI supports users in the Big Data cycle, including aggregation, storage, and retrieval of diverse data types from different data sources. This includes data management, context management, decision management, action management, and risk management.
Big Data can help alert problems and help find new solutions and get ideas about any new prospects. With the amount of information stream that comes in, it can be difficult to determine what is important and what isn’t. This is where AI and machine learning come in. It can help identify unusual patterns in the processes, help in the analysis, and suggest further steps to be taken.
It can also learn how users interact with analytics and learn subtle differences in meanings or context-specific nuances to understand numeric data sources. AI can also caution users about anomalies, unforeseen data patterns, monitoring events, and threats from system logs or social networking data.
Application of Big Data and Artificial Intelligence
After establishing how AI and Big Data work together, let us look at how some applications are benefitting from their synergy:
Banking and financial sectors
The banking and financial sectors apply these to monitor financial marketing activities. These institutions also use AI to keep an eye on any illegal trading activities. Trading data analytics are obtained for high-frequency trading, and decision making based on trading, risk analysis, and predictive analysis. It is also used for fraud warning and detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc.
AI has simplified health data prescriptions and health analysis, thus benefitting healthcare providers from the large data pool. Hospitals are using millions of collected data that allow doctors to use evidence-based medicine. Chronic diseases can be tracked faster by AI.
Manufacturing and supply chain
AI and Big Data in manufacturing, production management, supply chain management and analysis, and customer satisfaction techniques are flawless. The quality of products is thus much better with higher energy efficiency, reliable increase in levels, and profit increase.
Governments worldwide use AI applications like facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy explorations, environmental conservation, criminal investigations, and more.
Other sectors that use AI are mainly retail, entertainment, education, and more.
According to Gartner’s predictions, artificial intelligence will replace one in five workers by 2022. Firms and businesses can no longer afford to avoid using artificial intelligence and Big Data in their day-to-day. Investments in AI and Big Data analysis will be beneficial for everyone. Data sets will increase in the future, and with it, its application and investment will grow over time. Human relevance will continue to decrease as time goes by.
AI enables machine learning to be the future of the development of business technologies. It will automate data analysis and find new insights that were previously impossible to imagine by processing data manually. With machine learning, AI, and Big Data, we can redraw the way we approach everything else.
Frequently Asked Questions
Why does big data affect artificial intelligence?
Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents.
Is AI or big data better?
AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale.
Is AI used in big data?
When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI.
What is the future of AI in big data?
AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics.
"name": "Why does big data affect artificial intelligence?",
"text": "Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents."
"name": "Is AI or big data better?",
"text": "AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale."
"name": "Is AI used in big data?",
"text": "When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI."
"name": "What is the future of AI in big data?",
"text": "AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics."
Article | February 12, 2020
TinyML, as a concept, concerns the running of ML inference on Ultra Low-Power (ULP 1mW) microcontrollers found on IoT devices. Yet today, various challenges still limit the effective execution of TinyML in the embedded IoT world. As both a concept and community, it is still under development.Here at Ericsson, the focus of our TinyML as-a-Service (TinyMLaaS) activity is to democratize TinyML, enabling manufacturers to start their AI businesses using TinyML, which runs on 8, 16 and 32 bit microcontrollers.Our goal is to make the execution of ML tasks possible and easy in a specific class of devices. These devices are characterized by very constrained hardware and software resources such as sensor and actuator nodes based on these microcontrollers.Below, we present how we can bind the as-a-service model to TinyML. We will provide a high-level technical overview of our concept and introduce the design requirements and building blocks which characterize this emerging paradigm.
Article | March 23, 2021
Learn, re Learn and Unlearn
The times we are living in, we have to upgrade ourselves constantly in order to stay afloat with the industry be it Logistics, Traditional business, Agriculture, etc.. Technology is constantly changing our lives the way we used to live, living and will live. Anyone who thinks technology is not their cup of tea then I would say he /she will have no place in the world to live. It’s a blessing or curse on human race, only time will tell but effects are already surfacing in the market in the form of Job cut, poverty, some roles are no longer needed or replaced with.
Poor is getting poorer and rich is getting richer. Covid19 has not only brought the curse on human race but it has been a blessing in disguise for Tech giants and E-commerce. Technology not only changing the business but every human’s outlook towards life, family structure, the globalization of talents etc. It is nerve wrenching to imagine just what the world will look like in coming 20 years from now. Can all of us adapt to learn, re learn and unlearn quote? Or we have to depend upon countries/Governments to announce Minimum Wage to sustain our basic needs? Uncertainties are looming as the world is coming closer due to technology but emotionally going far. It’s sad to see children, colleagues communicating via emails and messages in the same home and office. Human is losing its touch and feel.
Repercussion to resists of learning, unlearning and relearning can bring down choices to none in the long run. Delay in adapting to change can be increasingly expensive as one can lose their place in a world earlier than one think. From 1992, where fewer people used to have facility of internet around , People used to stay in jobs for life but same people are now not wanted in the jobs when they go for interview as they lack in experience just because they have been doing what they were doing in one job without exposing themselves to the world’s new requirement of learn , re learn and unlearn. Chances of this group, getting a job will be negative. World has thrown different types of challenges to people, community, jobs, businesses , those people used to be applauded for remaining On one job for life ,same group of people are looked differently by corporate firms as redundant due to technology. So should people keep changing jobs after few years to just get on to learn, re learn and unlearn or continue waiting for their existing companies to face challenges and go off from the market? Only time and technology will determine what is store for human race next.
According to some of the studies, its shown the longer the delay in adopting technology for any given nation, the lower the per capita income of that nation. It shows extreme reliance on Technology but can all of us adopt to the technology at the same rate as its been introduced to us? Can our children or upcoming next generations adopt technology at same scale? Or future is Either Technology or nothing, in Short Job or Jobless there is no in between option?
Stephen Goldsmith, director of the Innovations in Government Program and Data-Smart City Solutions at the John F. Kennedy School of Government at Harvard University, said that in some areas, technological advancements have exceeded expectations made in 2000.
The Internet also has exploded beyond expectations. From 2000 to 2010, the number of Internet users increased 500 percent, from 361 million worldwide to almost 2 billion. Now, close to 4 billion people throughout the world use the Internet. People go online for everything from buying groceries and clothes to finding a date. They can register their cars online, earn a college degree, shop for houses and apply for a mortgage but again same question is arising , Can each one of us at the same scale use or advance their skill to use technology or we are leaving our senior generations behind and making them cripple in today’s society? Or How about Mid age people who are in their 50s and soon going to take over senior society , Can they get the job and advance their skill to meet technology demands or learn, unlearn and re learn or Not only pandemic but even Technology is going to make human redundant before their actual retirement and their knowledge, skill obsolete. There should be a way forward to achieve balance, absolute reliance on Technology is not only cyber threat to governments but in long term, Unemployment, Creating Jobs or paying minimum wage to unemployed mass will be a huge worry. At the end of the day, humans need basic and then luxury. Technology can bring ease of doing business, connecting businesses and out flows, connecting Wholesalers to end users but in between many jobs, heads will be slashed down and impact will be dire. Therefore Humans have to get themselves prepared to learn, unlearn and re learn to meet today’s technology requirement or prepare themselves for early retirement.
Article | December 10, 2020
Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker.
While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart.
In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well.
Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets.
He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years.
After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years.
He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him.
Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science.
He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology.
In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR.
During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models.
In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years.
At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases.
Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network.
Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product.
Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence.
His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career.
Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.